Author

A. Palladino

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 2

Signature Assays, Organisms & Methods

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Assay:ATAC-seq· 2 Assay:bulk RNA-seq· 2 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Research Focus:clustering· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

E. Khurana· 2 shared S. Cohen· 2 shared A. Gopalan· 1 shared A. N. Forbes· 1 shared A. Sboner· 1 shared C. E. Hill· 1 shared C. J. Lee· 1 shared C. K. Wong· 1 shared

Activity by Year

2020-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

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2 shared papersCancer Biology

E. Khurana

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2 papers

Recent·Most cited

Recapitulation of patient-specific 3D chromatin conformation using machine learning and validation of identified enhancer-gene targets

D. Xu, A. N. Forbes, S. Cohen, A. Palladino, T. Karadimitriou, E. Khurana

The paper develops a machine learning model to predict enhancer-gene connections from ATAC-seq and RNA-seq data, validated using multi-omics data and CRISPRi in cancer patients.

bioRxiv · Nov 16, 2021 · PreprintCancer Biology

Chromatin accessibility profiles of castration-resistant prostate cancers reveal novel subtypes and therapeutic vulnerabilities

F. Tang, S. Wang, C. K. Wong, C. J. Lee, S. Cohen, J. Park, C. E. Hill, K. Eng, R. Bareja, T. Han, E. M. Liu, A. Palladino, W. Di, D. Gao, W. Abida, , E. Khurana

This study identifies four subtypes of castration-resistant prostate cancer using ATAC-seq and RNA-seq data, revealing novel therapeutic targets and classification for 370 patients.

bioRxiv · Oct 26, 2020 · PreprintCancer Biology